VQQL. Applying Vector Quantization to Reinforcement Learning
نویسندگان
چکیده
Reinforcement learning has proven to be a set of successful techniques for finding optimal policies on uncertain and/or dynamic domains, such as the RoboCup. One of the problems on using such techniques appears with large state and action spaces, as it is the case of input information coming from the Robosoccer simulator. In this paper, we describe a new mechanism for solving the states generalization problem in reinforcement learning algorithms. This clustering mechanism is based on the vector quantization technique for signal analog-to-digital conversion and compression, and on the Generalized Lloyd Algorithm for the design of vector quantizers. Furthermore, we present the VQQL model, that integrates Q-Learning as reinforcement learning technique and vector quantization as state generalization technique. We show some results on applying this model to learning the interception task skill for Robosoccer agents.
منابع مشابه
Pursuit Reinforcement Competitive Learning: PRCL based Online Clustering with Learning Automata
A new online clustering method based on not only reinforcement and competitive learning but also pursuit algorithm (Pursuit Reinforcement Competitive Learning: PRCL) as well as learning automata is proposed for reaching a relatively stable clustering solution in comparatively short time duration. UCI repository data which are widely used for evaluation of clustering performance in usual is used...
متن کاملA Reinforcement Learning Approach to Online Clustering
A general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RGCL) ...
متن کاملCompetitive Reinforcement Learning in Continuous Control Tasks
This paper describes a novel hybrid reinforcement learning algorithm, Sarsa Learning Vector Quantization (SLVQ), that leaves the reinforcement part intact but employs a more effective representation of the policy function using a piecewise constant function based upon “policy prototypes.” The prototypes correspond to the pattern classes induced by the Voronoi tessellation generated by self-orga...
متن کامل